R/iarm.R

#' iarm: A package for item analysis in Rasch models
#'
#' Tools to assess model fit and identify misfitting items for Rasch models (RM) and partial
#' credit models (PCM). Included are item fit statistics, item-restscore association,
#' conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting.


#' @section Item Fit statistics:
#' Item fit statistics are used to assess whether individual items fit the Rasch model.
#' Outfit and infit  mean squares are well-known and much used statistics.
#'  They summarize standardized response residuals comparing observed responses to items
#'  to the expected responses.  To avoid bias expected responses are calculated under the
#'  conditional distribution of responses given the total score. Parametric bootstrapping is used to assess
#'  the significance of misfitting items. The item restscore gamma coefficient
#'  is used to assess differential item discrimination.
#'
#' @section Conditional likelihood ratio tests (CLR):
#' The conditional likelihood ratio test of Andersen is an overall test of fit of data to the model.
#' The test compares conditional maximum likelihood estimates of item parameters in different subgroups
#' to the estimates for the complete sample of persons. Subgroups are defined by outcomes of the total
#' score (test of homogeneity) or by outcomes of an exogenous variable (test of no differential item
#' functioning, DIF).
#'
#'
#' @docType package
#' @name iarm-package
#' @aliases iarm
#' @references  Andersen, E. B. (1973) A goodness of fit test for the Rasch model.
#' \emph{Psychometrika}, 38, 123-140.
#'
#' Kreiner, S. & Christensen, K. B. (2011) Exact evaluation of Bias in Rasch model residuals.
#' \emph{Advances in Mathematics Research}, 12, 19-40.
#'
#' Mueller, M. & Kreiner, S. (2015) Item Fit Statistics in Common Software for Rasch Analysis. Research Report 15-06, Department of Biostatistics, University of Copenhagen.
#'
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muellermarianne/iarm documentation built on June 7, 2018, 1:13 p.m.